diff --git a/README.md b/README.md
index 4fa3599780..798661f17d 100644
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+++ b/README.md
@@ -84,12 +84,31 @@ Code by default now uses [PyTorch 2.0](https://pytorch.org/get-started/pytorch-2
 
 ## todos
 
-A few that I'm aware of, other than the ones mentioned in code:
+A few todos I'm aware of:
+
+Optimizations
 
 - Additional optimizations to the running time
-- Report and track other metrics e.g. PPL
+- Investigate need for an actual Data Loader with a dedicated worker process for data
+- Look into more efficient fused optimizers (e.g. apex)
+- Re-evaluate use of flash attention (previously I wasn't able to get the forward pass to match up so I took it out)
+- CUDA Graphs?
+- Investigate potential speedups from Lightning or huggingface Accelerate
+
+Features / APIs
+
+- Add back fp16 support? (would need to also add back gradient scaler)
+- Add CPU support
+- Finetune the finetuning script, I think the hyperparams are not great
+- Replace poor man's configurator, and make sample.py configurable...
+- Report and track other metrics e.g. perplexity, num_tokens, MFU, ...
 - Eval zero-shot perplexities on PTB, WikiText, other related benchmarks
-- Current initialization (PyTorch default) departs from GPT-2. In a very quick experiment I found it to be superior to the one suggested in the papers, but that can't be right
-- Currently fp16 is much faster than bf16. Potentially revert back to using fp16 and re-introduce the gradient scaler?
-- Add some finetuning dataset and guide on some dataset for demonstration.
-- Reproduce GPT-2 results. It was estimated ~3 years ago that the training cost of 1.5B model was ~$50K
+
+Suspiciousness
+
+- Current initialization (PyTorch default) departs from GPT-2. In a very quick experiment I found it to be superior to the one suggested in the papers, but that can't be right?
+- I am still not 100% confident that my GPT-2 small reproduction hyperparameters are good, if someone has reproduced GPT-2 I'd be eager to exchange notes ty
+
+Results
+
+- Actually reproduce GPT-2 results and have clean configs that reproduce the result. It was estimated ~3 years ago that the training cost of 1.5B model was ~$50K (?). Sounds a bit too high.